code: def Accuracy(
      y_true: "ndarray", y_pred: "ndarray", multioutput=None
  ) -> Union[float64, "ndarray"]:
      """
      Calculate the accuracy score (Accuracy).
  
      .. math::
  
          \\text{Accuracy} = \\frac{1}{n}\\sum_{t=1}^n 1(y_t=\\hat{y_t})
  
      :param y_true: Array-like of shape = (n_samples, \*).
             Ground truth (correct) target values.
      :param y_pred: Array-like of shape = (n_samples, \*).
             Estimated target values.
  
      :return: Float or ndarray of floats.
               A non-negative floating point value (the best value is 1.0), or an
               array of floating point values, one for each individual target.
      """
      from sklearn.metrics._classification import accuracy_score
  
      y_true = np.squeeze(y_true)
      y_pred = np.squeeze(y_pred)
      if np.any(y_pred != y_pred.astype(int)):
          # y_pred is probability
          if y_pred.ndim == 1:
              y_pred = np.where(y_pred > 0.5, 1, 0)
          else:
              y_pred = np.argmax(y_pred, axis=1)
  
      return accuracy_score(y_true, y_pred)
  
imports: from sklearn.metrics import mean_squared_error
  from sklearn.metrics import r2_score
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_log_error
  import numpy
  import pandas
  from bigdl.dllib.utils.log4Error import *
  from bigdl.dllib.utils.log4Error import invalidInputError
  from numpy import float64, ndarray
  from typing import List, Tuple, Union, Optional, Callable, Any
label_desc: Use image node_id 14 for calling a global function with example usage: Accuracy(y_true, y_pred, multioutput) and returns: accuracy_score